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Kopicky L, Fan B, Valente SA. Intraoperative evaluation of surgical margins in breast cancer. Semin Diagn Pathol 2024:S0740-2570(24)00065-0. [PMID: 38965021 DOI: 10.1053/j.semdp.2024.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 06/12/2024] [Accepted: 06/20/2024] [Indexed: 07/06/2024]
Abstract
Achieving clear resection margins at the time of lumpectomy is essential for optimal patient outcomes. Margin status is traditionally determined by pathologic evaluation of the specimen and often is difficult or impossible for the surgeon to definitively know at the time of surgery, resulting in the need for re-operation to obtain clear surgical margins. Numerous techniques have been investigated to enhance the accuracy of intraoperative margin and are reviewed in this manuscript.
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Affiliation(s)
- Lauren Kopicky
- Division of Breast Surgical Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Betty Fan
- Department of Breast Surgery, University of Chicago, Chicago, IL, USA
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Hu K, Zhang X, Lee D, Xiong D, Zhang Y, Gao X. Boundary-Guided and Region-Aware Network With Global Scale-Adaptive for Accurate Segmentation of Breast Tumors in Ultrasound Images. IEEE J Biomed Health Inform 2023; 27:4421-4432. [PMID: 37310830 DOI: 10.1109/jbhi.2023.3285789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Breast ultrasound (BUS) image segmentation is a critical procedure in the diagnosis and quantitative analysis of breast cancer. Most existing methods for BUS image segmentation do not effectively utilize the prior information extracted from the images. In addition, breast tumors have very blurred boundaries, various sizes and irregular shapes, and the images have a lot of noise. Thus, tumor segmentation remains a challenge. In this article, we propose a BUS image segmentation method using a boundary-guided and region-aware network with global scale-adaptive (BGRA-GSA). Specifically, we first design a global scale-adaptive module (GSAM) to extract features of tumors of different sizes from multiple perspectives. GSAM encodes the features at the top of the network in both channel and spatial dimensions, which can effectively extract multi-scale context and provide global prior information. Moreover, we develop a boundary-guided module (BGM) for fully mining boundary information. BGM guides the decoder to learn the boundary context by explicitly enhancing the extracted boundary features. Simultaneously, we design a region-aware module (RAM) for realizing the cross-fusion of diverse layers of breast tumor diversity features, which can facilitate the network to improve the learning ability of contextual features of tumor regions. These modules enable our BGRA-GSA to capture and integrate rich global multi-scale context, multi-level fine-grained details, and semantic information to facilitate accurate breast tumor segmentation. Finally, the experimental results on three publicly available datasets show that our model achieves highly effective segmentation of breast tumors even with blurred boundaries, various sizes and shapes, and low contrast.
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3
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Reginelli A, Giacobbe G, Del Canto MT, Alessandrella M, Balestrucci G, Urraro F, Russo GM, Gallo L, Danti G, Frittoli B, Stoppino L, Schettini D, Iafrate F, Cappabianca S, Laghi A, Grassi R, Brunese L, Barile A, Miele V. Peritoneal Carcinosis: What the Radiologist Needs to Know. Diagnostics (Basel) 2023; 13:diagnostics13111974. [PMID: 37296826 DOI: 10.3390/diagnostics13111974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis.
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Affiliation(s)
- Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giuliana Giacobbe
- General and Emergency Radiology Department, "Antonio Cardarelli" Hospital, 80131 Naples, Italy
| | - Maria Teresa Del Canto
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Marina Alessandrella
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giovanni Balestrucci
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Fabrizio Urraro
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Gaetano Maria Russo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Luigi Gallo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Ginevra Danti
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, 50134 Florence, Italy
| | - Barbara Frittoli
- Department of Radiology, Spedali Civili Hospital, 25123 Brescia, Italy
| | - Luca Stoppino
- Department of Radiology, University Hospital of Foggia, 71122 Foggia, Italy
| | - Daria Schettini
- Department of Radiology, Villa Scassi Hospital, Corso Scassi 1, 16121 Genova, Italy
| | - Franco Iafrate
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza-University of Rome, Radiology Unit-Sant'Andrea University Hospital, 00189 Rome, Italy
| | - Roberto Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100 L'Aquila, Italy
| | - Vittorio Miele
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
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Manhoobi IP, Bodilsen A, Nijkamp J, Pareek A, Tramm T, Redsted S, Christiansen P. Diagnostic accuracy of radiography, digital breast tomosynthesis, micro-CT and ultrasound for margin assessment during breast surgery: A systematic review and meta-analysis. Acad Radiol 2022; 29:1560-1572. [PMID: 34996687 DOI: 10.1016/j.acra.2021.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/29/2021] [Accepted: 12/06/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES Achieving adequate resection margins in breast conserving surgery is challenging and often demands more than one surgical procedure. We evaluated pooled diagnostic sensitivity, and specificity of radiological methods for intraoperative margin assessment and their impact on repeat surgery rate. MATERIALS AND METHODS We included studies using radiography, digital breast tomosynthesis (DBT), micro-CT, and ultrasound for intraoperative margin assessment with the histological assessment as the reference method. A systematic search was performed in PubMed, Embase, Cochrane Library, Scopus, and Web of Science. Two investigators screened the studies for eligibility criteria and extracted data of the included studies independently. The quality assessment on diagnostic accuracy studies (QUADAS)-2 tool was used. A bivariate random effect model was used to obtained pooled sensitivity and specificity of the index tests in the meta-analysis. RESULTS The systematic search resulted in screening of 798 unique records. Twenty-two articles with 29 radiological imaging methods were selected for meta-analysis. Pooled sensitivity and specificity and area under the curve were calculated for each of the 4 subgroups in the meta-analysis respectively: Radiography; 52%, 77%, 60%, DBT; 67%, 76%, 76%, micro-CT; 68%, 69%, 72%, and ultrasound; 72%, 78%, 80%. The repeat surgery rate was poorly reported in the included studies. CONCLUSION Ultrasound showed the highest and radiography the lowest diagnostic performance for intraoperative margin assessment. However, the heterogeneity between studies was high and the subgroups small. The radiological methods for margin assessment need further improvement to provide reliable guidance in the clinical workflow and to prevent repeat surgeries.
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Affiliation(s)
| | - Anne Bodilsen
- Department of Abdominal Surgery (A.B.), Aarhus University Hospital, Denmark
| | - Jasper Nijkamp
- Danish center for Particle Therapy (J.N.), Aarhus University Hospital, Department of Clinical Medicine, Aarhus University, Denmark
| | - Anuj Pareek
- Department of Radiology (A.P.), North Zealand Hospital, Denmark
| | - Trine Tramm
- Department of Pathology (T.T.), Aarhus University Hospital, Denmark
| | - Søren Redsted
- Department of Radiology, (I.P.M., S.R.), Aarhus University Hospital, Denmark
| | - Peer Christiansen
- Department of Plastic and Breast Surgery (P.C.), Aarhus University Hospital, Denmark
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Supine versus Prone 3D Abus Accuracy in Breast Tumor Size Evaluation. Tomography 2022; 8:1997-2009. [PMID: 36006065 PMCID: PMC9413588 DOI: 10.3390/tomography8040167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Breast-conserving surgery (BCS) with negative resection margins decreases the locoregional recurrence rate. Breast cancer size is one of the main determinants of Tumor-Node-Metastasis (TNM) staging. Our study aimed to investigate the accuracy of supine 3D automated breast ultrasound (3D ABUS) compared to prone 3D ABUS in the evaluation of tumor size in breast cancer patient candidates for BCS. In this prospective two-center study (Groups 1 and 2), we enrolled patients with percutaneous biopsy-proven early-stage breast cancer, in the period between June 2019 and May 2020. Patients underwent hand-held ultrasound (HHUS), contrast-enhanced magnetic resonance imaging (CE-MRI) and 3D ABUS—supine 3D ABUS in Group 1 and prone 3D ABUS in Group 2. Histopathological examination (HE) was considered the reference standard. Bland–Altman analysis and plots were used. Eighty-eight patients were enrolled. Compared to prone, supine 3D ABUS showed better agreement with HE, with a slight tendency toward underestimation (mean difference of −2 mm). Supine 3D ABUS appears to be a useful tool and more accurate than HHUS in the staging of breast cancer.
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Di Grezia G. Special Issue “Advancement in Breast Diagnostic and Interventional Radiology”. Diagnostics (Basel) 2022; 12:diagnostics12010217. [PMID: 35054384 PMCID: PMC8774975 DOI: 10.3390/diagnostics12010217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 01/13/2022] [Indexed: 02/04/2023] Open
Affiliation(s)
- Graziella Di Grezia
- Scientific Responsible of Breast Screening Program, Radiology Division, "Criscuoli-Frieri" Hospital, 83054 Sant' Angelo dei Lombardi, AV, Italy
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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Belfiore MP, Reginelli A, Russo A, Russo GM, Rocco MP, Moscarella E, Ferrante M, Sica A, Grassi R, Cappabianca S. Usefulness of High-Frequency Ultrasonography in the Diagnosis of Melanoma: Mini Review. Front Oncol 2021; 11:673026. [PMID: 34178660 PMCID: PMC8226081 DOI: 10.3389/fonc.2021.673026] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/17/2021] [Indexed: 01/30/2023] Open
Abstract
High-frequency equipment is characterized by ultrasound probes with frequencies of over 10 MHz. At higher frequencies, the wavelength decreases, which determines a lower penetration of the ultrasound beam so as to offer a better evaluation of the surface structures. This explains the growing interest in ultrasound in dermatology. This review examines the state of the art of high-frequency ultrasound (HFUS) in the assessment of skin cancer to ensure the high clinical approach and provide the best standard of evidence on which to base clinical and policy decisions.
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Affiliation(s)
- Maria Paola Belfiore
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Anna Russo
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Gaetano Maria Russo
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Maria Paola Rocco
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Elvira Moscarella
- Dermatology Unit, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Marilina Ferrante
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Antonello Sica
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Roberto Grassi
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
- Italian Society of Medical Radiology (SIRM) Foundation, Milan, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
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Qu X, Shi Y, Hou Y, Jiang J. An attention-supervised full-resolution residual network for the segmentation of breast ultrasound images. Med Phys 2020; 47:5702-5714. [PMID: 32964449 DOI: 10.1002/mp.14470] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 08/07/2020] [Accepted: 08/10/2020] [Indexed: 01/22/2023] Open
Abstract
PURPOSE Breast cancer is the most common cancer among women worldwide. Medical ultrasound imaging is one of the widely applied breast imaging methods for breast tumors. Automatic breast ultrasound (BUS) image segmentation can measure the size of tumors objectively. However, various ultrasound artifacts hinder segmentation. We proposed an attention-supervised full-resolution residual network (ASFRRN) to segment tumors from BUS images. METHODS In the proposed method, Global Attention Upsample (GAU) and deep supervision were introduced into a full-resolution residual network (FRRN), where GAU learns to merge features at different levels with attention for deep supervision. Two datasets were employed for evaluation. One (Dataset A) consisted of 163 BUS images with tumors (53 malignant and 110 benign) from UDIAT Centre Diagnostic, and the other (Dataset B) included 980 BUS images with tumors (595 malignant and 385 benign) from the Sun Yat-sen University Cancer Center. The tumors from both datasets were manually segmented by medical doctors. For evaluation, the Dice coefficient (Dice), Jaccard similarity coefficient (JSC), and F1 score were calculated. RESULTS For Dataset A, the proposed method achieved higher Dice (84.3 ± 10.0%), JSC (75.2 ± 10.7%), and F1 score (84.3 ± 10.0%) than the previous best method: FRRN. For Dataset B, the proposed method also achieved higher Dice (90.7 ± 13.0%), JSC (83.7 ± 14.8%), and F1 score (90.7 ± 13.0%) than the previous best methods: DeepLabv3 and dual attention network (DANet). For Dataset A + B, the proposed method achieved higher Dice (90.5 ± 13.1%), JSC (83.3 ± 14.8%), and F1 score (90.5 ± 13.1%) than the previous best method: DeepLabv3. Additionally, the parameter number of ASFRRN was only 10.6 M, which is less than those of DANet (71.4 M) and DeepLabv3 (41.3 M). CONCLUSIONS We proposed ASFRRN, which combined with FRRN, attention mechanism, and deep supervision to segment tumors from BUS images. It achieved high segmentation accuracy with a reduced parameter number.
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Affiliation(s)
- Xiaolei Qu
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, 100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China
| | - Yao Shi
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, 100191, China
| | - Yaxin Hou
- Department of Diagnostic Ultrasound, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
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Renzulli M, Zanotti S, Clemente A, Mineo G, Tovoli F, Reginelli A, Barile A, Cappabianca S, Taffurelli M, Golfieri R. Hereditary breast cancer: screening and risk reducing surgery. Gland Surg 2019; 8:S142-S149. [PMID: 31559181 PMCID: PMC6755941 DOI: 10.21037/gs.2019.04.04] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND The screening modalities for women at high risk for breast cancer has received an increasing role during the last years. The aim of this study was to evaluate the performance of our screening program comparing the diagnostic sensitivity of clinical breast examination, mammography, ultrasonography (US) and magnetic resonance imaging (MRI). METHODS Clinical Breast examination, mammography, US and MRI for each patient with BRCA1 and BRCA2 mutation who underwent breast surgery in our Institution from October 2008 to April 2016 were retrospectively evaluated. The diagnostic accuracy for MRI and for the other surveillance tests in identifying early breast cancer were assessed. RESULTS Twenty-six female patients with genetic mutation underwent breast surgery. Twenty-two out of 26 (85%) developed cancer during the dedicated screening protocol whereas 4 women who underwent surgery did not have cancer. Imaging was able to detect cancer in all 22 patients (per patient sensibility of 100%), identifying all 35 neoplastic lesions (per lesion sensibility of 100%). The combination of Clinical Breast Examination, US and mammography aided the cancer diagnosis in 14 (64%) of patients with a sensitivity of 64% and specificity of 100%. MRI identified all the cancers, with sensibility and specificity of 100%. Moreover, in 8 (36%) of the 22 patients who developed breast cancers, the cancers were detected only by MRI, revealing a significant superiority respect to the other surveillance modalities (P<0.05). CONCLUSIONS MRI demonstrated to be the best imaging modality in detection of breast cancer even for lesion <1 cm. Prophylactic mastectomy is the most effective risk reduction strategy in women at high risk, contributing to the reduction of anxiety related to the condition of a carrier.
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Affiliation(s)
- Matteo Renzulli
- Radiology Unit, Department of Experimental, Diagnostic and Speciality Medicine, Sant’Orsola Hospital, University of Bologna, Bologna, Italy
| | - Simone Zanotti
- Breast Unit, Department of Woman, Child and Urological Diseases, Sant’Orsola Hospital, University of Bologna, Bologna, Italy
| | - Alfredo Clemente
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy
| | - Giangaspare Mineo
- Radiology Unit, Department of Diagnostic Medicine and Prevention, Sant’Orsola Hospital, University of Bologna, Bologna, Italy
| | - Francesco Tovoli
- Unit of Internal Medicine, Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Alfonso Reginelli
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy
| | - Antonio Barile
- Department of Biotechnology and Applied Clinical Sciences, University of L’Aquila, S. Salvatore Hospital, L’Aquila, Italy
| | - Salvatore Cappabianca
- Radiology and Radiotherapy Unit, Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy
| | - Mario Taffurelli
- Breast Unit, Department of Woman, Child and Urological Diseases, Sant’Orsola Hospital, University of Bologna, Bologna, Italy
| | - Rita Golfieri
- Radiology Unit, Department of Experimental, Diagnostic and Speciality Medicine, Sant’Orsola Hospital, University of Bologna, Bologna, Italy
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State-of-the-Art in Integrated Breast Imaging. BIOMED RESEARCH INTERNATIONAL 2019; 2019:7596059. [PMID: 30723743 PMCID: PMC6339726 DOI: 10.1155/2019/7596059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Accepted: 12/24/2018] [Indexed: 11/25/2022]
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